A Comparative Study of Recommendation Methods for Mobile OSN Users

Online Social Networks (OSNs) have grown a lot from the past and now it is becoming more and more popular in today’s life. The main problem facing by today’s networks is “information overloading”. A solution to this problem is a good recommendation system. It automatically suggests items to a user that he/she is having interest in it. To improve the accuracy and precision of recommendation systems, we have to incorporate information from social network. If so, the system will automatically monitors the social activities of users and ranks other users and recommends items/users to a target user based on this information. We provide a brief idea about the tasks of recommender system and how the concept of social interaction can be utilised to make recommendation. Here we make a comparative study of various recommendation systems which uses social data for normal users and mobile users. We also discuss about the traditional recommendation systems that do not uses social information also.